Isolation Forest

Isolation Forest is an anomaly detection algorithm that leverages the principle that outliers are easier to isolate than inliers through random partitioning of data. Current research focuses on improving its performance and interpretability, including developing variations like Signature Isolation Forest for functional data and Attention-Based Isolation Forest for enhanced accuracy, as well as exploring optimal tree structures and alternative scoring methods. These advancements enhance the algorithm's applicability across diverse fields, from cybersecurity and financial risk assessment to exoplanet research and improving the explainability of anomaly detection models within decision support systems.